FineVess/fractal_dimension.py at master · PRBioimages/FineVess · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
import numpy as np
import argparse
import os
import numpy as np
from matplotlib import pyplot as plt
from batchgenerators.utilities.file_and_folder_operations import *
import scipy.misc
from skimage.morphology import skeletonize_3d
from scipy import ndimage as ndi
from dvn.utils import get_itk_array, make_itk_image, write_itk_image, get_itk_image
# from batchgenerators.utilities.file_and_folder_operations import *


def fractal_dimension(image):
    binary_image = image > np.mean(image)
    sizes = 2 ** np.arange(1, 8)
    counts = []
    for size in sizes:
        count = 0
        for i in range(0, image.shape[0], size):
            for j in range(0, image.shape[1], size):
                if np.any(binary_image[i:i + size, j:j + size]):
                    count += 1
        counts.append(count)

    coeffs = np.polyfit(np.log(sizes), np.log(counts), 1)
    dimension = -coeffs[0]

    return dimension


def parse_args():
    parser = argparse.ArgumentParser(description='analyse binary vessel segmentation')
    # parser.add_argument('--filenames1', dest='filenames1', type=str,
    #                     default='/public/yangxiaodu/vessap2/data_self/raw_seg_vessels_result.txt')
    # # parser.add_argument('--test_labelFns', dest='test_labelFns', type=str, default='testing_labels_self.txt')
    # parser.add_argument('--filenames2', dest='filenames2', type=str,
    #                     default='/public/yangxiaodu/vessap2/data_self/preprocessed_seg_vessels_result.txt')
    # parser.add_argument('--maskFilename', dest='maskFn', type=str,
    #                     default=None,
    #                     help='a mask file to be applied to the predictions')
    # parser.add_argument('--output', dest='output', type=str,
    #                     default='/public/yangxiaodu/nnunet/save_data/nnUNet_trained_models/nnUNet/3d_fullres/Task508_other_vessels_raw/nnUNetTrainerV2_loss_ignore_label2_cew_sb_CBAM_decoder_btc_moreDA_BN__nnUNetPlansv2.1/new_all_pre_add_out',
    #                     help='output folder for storing predictions (default: current working directory)')
    parser.add_argument('--f', dest='format', type=str, default='.nii.gz',
                        help='NIFTI file format for saving outputs (default: .nii.gz)')
    parser.add_argument('--txt', dest='txt', type=bool, default=False,
                        help='choose iuput way. If using txt files, choose txt=True (default: .nii.gz)')
    args = parser.parse_args()

    return args


def save_data(data, img, filename):
    out_img = make_itk_image(data, img)
    write_itk_image(out_img, filename)

def run():
    args = parse_args()
    # outputFn = args.output
    txt=args.txt
    fmt = args.format
    # filenames1 = args.filenames1
    # filenames2 = args.filenames2
    # masks = args.maskFn


    print('----------------------------------------')
    print(' Postprocessing Parameters ')
    print('----------------------------------------')
    print('txt',txt)
    # print('Input files:', filenames1)
    # print('Input files:', filenames2)
    # print('Mask file:', masks)
    # print('Output folder:', outputFn)

    print('Output format:', fmt)


    # with open(os.path.abspath(args.filenames1)) as f:
    #     iFn1 = f.readlines()
    # iFn1 = [x.strip() for x in iFn1]
    #
    # with open(os.path.abspath(args.filenames2)) as f:
    #     iFn2 = f.readlines()
    # iFn2 = [x.strip() for x in iFn2]
    # if masks is not None:
    #     with open(os.path.abspath(args.maskFn)) as f:
    #         mFn = f.readlines()
    #     mFn = [x.strip() for x in mFn]
    # else:
    #     mFn=[]


    source_folder = '/public/yangxiaodu/clearmap/data/postprocess_all1code/analysis'
    train_cases = subfiles(join(source_folder, 'fractal'), suffix=".nii.gz", join=False)
    if txt==False:
        iFn1=[]
        iFn2=[]
        for i, t in enumerate(train_cases):
            iFn1.append(join(source_folder, 'fractal',t))

        for ifn1 in iFn1:
            prefix = os.path.basename(ifn1).split('.')[0]
            refined_patch = get_itk_array(ifn1)
            dimension = fractal_dimension(refined_patch)
            print(prefix, dimension)


if __name__ == '__main__':
    run()